Triple
T21948990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erqi Memorial Tower |
E542010
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Zhengzhou |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Zhengzhou | Statement: [Erqi Memorial Tower, locatedIn, Zhengzhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhengzhou Context triple: [Erqi Memorial Tower, locatedIn, Zhengzhou]
-
A.
Zhengzhou
chosen
Zhengzhou is a major city in central China that serves as the capital of Henan Province and an important national transportation and industrial hub.
-
B.
Zhoukou
Zhoukou is a prefecture-level city in eastern Henan Province, China, known as an important agricultural and transportation hub with historical and cultural significance.
-
C.
Xinzheng
Xinzheng is a county-level city in central China's Henan Province, known as part of the Zhengzhou metropolitan area and as the site of Zhengzhou Xinzheng International Airport.
-
D.
Zhumadian
Zhumadian is a prefecture-level city in southern Henan Province, China, known as an important regional transport and agricultural hub.
-
E.
Xuchang
Xuchang is a historically significant city in central China, known as a former capital during the Three Kingdoms period and now an important industrial and transportation hub.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0c47ef0e48190a50e1bcc43f4b3fd |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1243aed048190b4342899c83b38ec |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:58 p.m.